






from random import shuffle

import tensorflow as tf
from numpy import array




def 				A		(					lowercase__  :  str ,		lowercase__  :  Optional[int]  )		-> str:
	UpperCamelCase__ :Any						=      int(lowercase__  )
	assert noofclusters < len(lowercase__  )

	# Find out the dimensionality
	UpperCamelCase__ :str						=      len(vectors[0]  )

	# Will help select random centroids from among the available vectors
	UpperCamelCase__ :Dict						=      list(range(len(lowercase__  )  )  )
	shuffle(lowercase__  )

	# GRAPH OF COMPUTATION
	# We initialize a new graph and set it as the default during each run
	# of this algorithm. This ensures that as this function is called
	# multiple times, the default graph doesn't keep getting crowded with
	# unused ops and Variables from previous function calls.

	UpperCamelCase__ :List[Any]						=      tf.Graph()

	with graph.as_default():
		# SESSION OF COMPUTATION

		UpperCamelCase__ :Dict						=      tf.Session()

		##CONSTRUCTING THE ELEMENTS OF COMPUTATION

		##First lets ensure we have a Variable vector for each centroid,
		##initialized to one of the vectors from the available data points
		UpperCamelCase__ :Optional[Any]						=      [
		    tf.Variable(vectors[vector_indices[i]]  ) for i in range(lowercase__  )
		]
		##These nodes will assign the centroid Variables the appropriate
		##values
		UpperCamelCase__ :Optional[Any]						=      tf.placeholder("""float64""" ,		[dim]  )
		UpperCamelCase__ :List[Any]						=      []
		for centroid in centroids:
			cent_assigns.append(tf.assign(lowercase__ ,		lowercase__  )  )

		##Variables for cluster assignments of individual vectors(initialized
		##to 0 at first)
		UpperCamelCase__ :str						=      [tf.Variable(0  ) for i in range(len(lowercase__  )  )]
		##These nodes will assign an assignment Variable the appropriate
		##value
		UpperCamelCase__ :Union[str, Any]						=      tf.placeholder("""int32"""  )
		UpperCamelCase__ :str						=      []
		for assignment in assignments:
			cluster_assigns.append(tf.assign(lowercase__ ,		lowercase__  )  )

		##Now lets construct the node that will compute the mean
		# The placeholder for the input
		UpperCamelCase__ :int						=      tf.placeholder("""float""" ,		[None, dim]  )
		# The Node/op takes the input and computes a mean along the 0th
		# dimension, i.e. the list of input vectors
		UpperCamelCase__ :List[str]						=      tf.reduce_mean(lowercase__ ,		0  )

		##Node for computing Euclidean distances
		# Placeholders for input
		UpperCamelCase__ :str						=      tf.placeholder("""float""" ,		[dim]  )
		UpperCamelCase__ :Any						=      tf.placeholder("""float""" ,		[dim]  )
		UpperCamelCase__ :List[Any]						=      tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase__ ,		lowercase__  ) ,		2  )  )  )

		##This node will figure out which cluster to assign a vector to,
		##based on Euclidean distances of the vector from the centroids.
		# Placeholder for input
		UpperCamelCase__ :Dict						=      tf.placeholder("""float""" ,		[noofclusters]  )
		UpperCamelCase__ :Optional[int]						=      tf.argmin(lowercase__ ,		0  )

		##INITIALIZING STATE VARIABLES

		##This will help initialization of all Variables defined with respect
		##to the graph. The Variable-initializer should be defined after
		##all the Variables have been constructed, so that each of them
		##will be included in the initialization.
		UpperCamelCase__ :Union[str, Any]						=      tf.initialize_all_variables()

		# Initialize all variables
		sess.run(lowercase__  )

		##CLUSTERING ITERATIONS

		# Now perform the Expectation-Maximization steps of K-Means clustering
		# iterations. To keep things simple, we will only do a set number of
		# iterations, instead of using a Stopping Criterion.
		UpperCamelCase__ :Dict						=      100
		for _ in range(lowercase__  ):
			##EXPECTATION STEP
			##Based on the centroid locations till last iteration, compute
			##the _expected_ centroid assignments.
			# Iterate over each vector
			for vector_n in range(len(lowercase__  )  ):
				UpperCamelCase__ :Optional[Any]						=      vectors[vector_n]
				# Compute Euclidean distance between this vector and each
				# centroid. Remember that this list cannot be named
				#'centroid_distances', since that is the input to the
				# cluster assignment node.
				UpperCamelCase__ :str						=      [
				    sess.run(lowercase__ ,		feed_dict={va: vect, va: sess.run(lowercase__  )}  )
				    for centroid in centroids
				]
				# Now use the cluster assignment node, with the distances
				# as the input
				UpperCamelCase__ :Optional[int]						=      sess.run(
				    lowercase__ ,		feed_dict={centroid_distances: distances}  )
				# Now assign the value to the appropriate state variable
				sess.run(
				    cluster_assigns[vector_n] ,		feed_dict={assignment_value: assignment}  )

			##MAXIMIZATION STEP
			# Based on the expected state computed from the Expectation Step,
			# compute the locations of the centroids so as to maximize the
			# overall objective of minimizing within-cluster Sum-of-Squares
			for cluster_n in range(lowercase__  ):
				# Collect all the vectors assigned to this cluster
				UpperCamelCase__ :Any						=      [
				    vectors[i]
				    for i in range(len(lowercase__  )  )
				    if sess.run(assignments[i]  ) == cluster_n
				]
				# Compute new centroid location
				UpperCamelCase__ :Optional[int]						=      sess.run(
				    lowercase__ ,		feed_dict={mean_input: array(lowercase__  )}  )
				# Assign value to appropriate variable
				sess.run(
				    cent_assigns[cluster_n] ,		feed_dict={centroid_value: new_location}  )

        # Return centroids and assignments
		UpperCamelCase__ :Optional[Any]						=      sess.run(lowercase__  )
		UpperCamelCase__ :Dict						=      sess.run(lowercase__  )
		return centroids, assignments



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[str] 			=		'xmod'




			def __init__(				self   ,    snake_case=30522   ,    snake_case=768   ,    snake_case=12   ,    snake_case=12   ,    snake_case=3072   ,    snake_case="gelu"   ,    snake_case=0.1   ,    snake_case=0.1   ,    snake_case=512   ,    snake_case=2   ,    snake_case=0.02   ,    snake_case=1E-1_2   ,    snake_case=1   ,    snake_case=0   ,    snake_case=2   ,    snake_case="absolute"   ,    snake_case=True   ,    snake_case=None   ,    snake_case=False   ,    snake_case=2   ,    snake_case=False   ,    snake_case=True   ,    snake_case=True   ,    snake_case=("en_XX",)   ,    snake_case=None   ,    **snake_case   ,    ):

						'''simple docstring'''


						super().__init__(pad_token_id=snake_case   ,    bos_token_id=snake_case   ,    eos_token_id=snake_case   ,    **snake_case  )

						UpperCamelCase__       =							vocab_size
						UpperCamelCase__       =							hidden_size
						UpperCamelCase__       =							num_hidden_layers
						UpperCamelCase__       =							num_attention_heads
						UpperCamelCase__       =							hidden_act
						UpperCamelCase__       =							intermediate_size
						UpperCamelCase__       =							hidden_dropout_prob
						UpperCamelCase__       =							attention_probs_dropout_prob
						UpperCamelCase__       =							max_position_embeddings
						UpperCamelCase__       =							type_vocab_size
						UpperCamelCase__       =							initializer_range
						UpperCamelCase__       =							layer_norm_eps
						UpperCamelCase__       =							position_embedding_type
						UpperCamelCase__       =							use_cache
						UpperCamelCase__       =							classifier_dropout
						UpperCamelCase__       =							pre_norm
						UpperCamelCase__       =							adapter_reduction_factor
						UpperCamelCase__       =							adapter_layer_norm
						UpperCamelCase__       =							adapter_reuse_layer_norm
						UpperCamelCase__       =							ln_before_adapter
						UpperCamelCase__       =							list(snake_case  )
						UpperCamelCase__       =							default_language







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if self.task == "multiple-choice":
									UpperCamelCase__       =							{0: "batch", 1: "choice", 2: "sequence"}
						else:
									UpperCamelCase__       =							{0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]  )


